Towards In Silico identification of genes contributing to similarity of patients’ multi-omics profiles: a case study of acute myeloid leukemia

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Batten, DJ, Crofts, JJ ORCID: https://orcid.org/0000-0001-7751-9984 and Chuzhanova, N ORCID: https://orcid.org/0000-0002-4655-3618, 2023. Towards In Silico identification of genes contributing to similarity of patients’ multi-omics profiles: a case study of acute myeloid leukemia. Genes, 14 (9): 1795.

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Official URL: https://doi.org/10.3390/genes14091795

Abstract

We propose a computational framework for selecting biologically plausible genes identified by clustering of multi-omics data that reveal patients' similarity, thus giving researchers a more comprehensive view on any given disease. We employ spectral clustering of a similarity network created by fusion of three similarity networks, based on mRNA expression of immune genes, miRNA expression and DNA methylation data, using SNF_v2.1 software. For each cluster, we rank multi-omics features, ensuring the best separation between clusters, and select the top-ranked features that preserve clustering. To find genes targeted by DNA methylation and miRNAs found in the top-ranked features, we use chromosome-conformation capture data and miRNet 2.0 software, respectively. To identify informative genes, these combined sets of target genes are analyzed in terms of their enrichment in somatic/germline mutations, GO biological processes/pathways terms and known sets of genes considered to be important in relation to a given disease, as recorded in the Molecular Signature Database from GSEA. The protein-protein interaction (PPI) networks were analyzed to identify genes that are hubs of PPI networks. We used data recorded in The Cancer Genome Atlas for patients with acute myeloid leukemia to demonstrate our approach, and discuss our findings in the context of results in the literature.

Item Type:	Journal article
Publication Title:	Genes
Creators:	Batten, D.J., Crofts, J.J. and Chuzhanova, N.
Publisher:	MDPI-AG
Date:	2023
Volume:	14
Number:	9
Identifiers:	Number Type 10.3390/genes14091795 DOI 1804167 Other
Rights:	© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	13 Sep 2023 11:06
Last Modified:	13 Sep 2023 11:06
Related URLs:	Publisher
URI:	https://irep.ntu.ac.uk/id/eprint/49694